Odours in sewer networks: nuisance assessment

2013 ◽  
Vol 67 (3) ◽  
pp. 543-548 ◽  
Author(s):  
A. Pérez ◽  
C. Manjón ◽  
J. V. Martínez ◽  
J. M. Juárez-Galan ◽  
B. Barillon ◽  
...  
Keyword(s):  
State Of The Art ◽  
Public Image ◽  
Site Specific ◽  
The Public ◽  
Sewer Network ◽  
Pumping Stations ◽  
Different Types ◽  
Odour Concentration ◽  
Sewer Networks

As odour nuisance can affect the quality of life, the population is more and more demanding and in many cities sewers are a critical source of odours. Both factors can lead to increasing numbers of complaints due to the odour nuisance perceived by the residents, affecting also the public image of the sewer management companies. Odours associated with sewer networks are very heterogeneous, in as much as the different ‘types of odours’ encountered are sewer site specific. The state of the art indicates that there are three parameters that play an important role with the nuisance generated by an odour: hedonic odour tone, odour concentration and odour intensity. This paper presents the results of the study on odour nuisance carried out in different points of the sewer network, with the aim to assess the nuisance generated and identify which points of the sewer should be targeted to implement corrective actions. Considering the different parameters assessed, pumping stations have been identified as critical points of odour nuisance in the sewers, being recommended to implement an odour treatment system in order to guarantee the odour comfort of residents.

Multisensor systems based on phthalocyanines for monitoring the quality of grapes

2016 ◽  
Vol 20 (08n11) ◽  
pp. 889-894 ◽  
Author(s):  
Maria Luz Rodriguez-Mendez ◽  
Celia García-Hernandez ◽  
Cristina Medina-Plaza ◽  
Cristina García-Cabezón ◽  
Jose Antonio de Saja
Keyword(s):  
State Of The Art ◽  
Layer By Layer ◽  
Carbon Paste ◽  
Electron Mediator ◽  
Multisensor Systems ◽  
Langmuir Blodgett ◽  
Different Types ◽  
Production Stages ◽  
Nanostructured Sensors

Arrays of phthalocyanine-based sensors with complementary activity have been used to develop voltammetric electronic tongues. Such systems have demonstrated to be useful in enology for the evaluation of quality of wines in different production stages, from grapes to bottles. In this paper, the state of the art of multisensor systems based on phthalocyanines dedicated to the analysis of musts (juices obtained from crushed grapes) is described. Such multisensor systems cover different types of sensors from simple Carbon Paste Electrodes, to sophiticated nanostructured sensors, including Langmuir–Blodgett or Layer by Layer thin films and biomimetic biosensors where phthalocyanines play a crucial role as electron mediator between enzymes and electrodes. In all cases, multisensor systems based on phthalocyanines have been able to discriminate musts prepared from different varieties of grapes. The performance of these systems can be improved by combining non-specific sensors with biosensors containing enzymes selective to phenols. In this case, excellent relationships have been found between the responses provided by the array and the content in phenols and acids provided by traditional chemical analysis.

Characteristic Seismic Failures of Buildings

2019 ◽  
Author(s):  
V. J. "Jon" Moseley ◽  
Andreas Lampropoulos ◽  
Eftychia Apostolidi ◽  
Christos Giarlelis
Keyword(s):  
Structural Engineering ◽  
State Of The Art ◽  
Seismic Retrofitting ◽  
Background Information ◽  
Financial Loss ◽  
The Public ◽  
Different Types ◽  
Sufficient Degree ◽  
First Time ◽  
Failure Types

<p>Earthquakes can cause considerable fatalities, injuries and financial loss. The forces of nature cannot be blamed, as the problem lies with the structures in seismic regions that may not have been designed or constructed to a sufficient degree to resist earthquake actions or they may have design flaws. This Structural Engineering Document (SED) concerns reinforced concrete and masonry buildings together with geotechnical aspects and presents in a concise and practical way the state of the art of current understanding of building failures due to earthquakes. It classifies the different types of seismic failure, explains the reasons for each failure, describes good practices to avoid such failures and also describes seismic retrofitting/upgrading procedures for pre-earthquake strengthening and post-earthquake repair and/or strengthening techniques for deficient buildings. Carefully selected photographs and diagrams illustrate the different failure types. This document could be considered as quite unique, as this is the first time such material concerning characteristic seismic failures of buildings has been presented together in one single document. It is intended to be a valuable educational reference textbook aimed at all levels of experience of engineers. It provides background information, ideas, guidance and reassurance to engineers in earthquake regions faced with the task of building a safer future for the public and to protect lives. <p> <iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/Oddi3VTtxCM" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

Supervision of Pumps and their Operating Conditions in Sewage Pumping Stations

2010 ◽  
Vol 5 (2) ◽  
Author(s):  
Carsten Skovmose Kallesøe ◽  
Mick Eriksen
Keyword(s):  
Energy Consumption ◽  
Operating Conditions ◽  
Optimal Scheduling ◽  
Pumping Station ◽  
Sewer Network ◽  
Pump Station ◽  
Pumping Stations ◽  
Innovative Solutions ◽  
System Optimal ◽  
Sewer Networks

The main energy consumers in sewer networks are the sewage pumps. Therefore, to minimize the energy consumption, it is essential that these pumps operate under satisfactory conditions. Knowledge about the efficiency of the pumps and their operating conditions can help the pump station management to operate the system optimal. In the search for innovative solutions that can help the sewer management with this information, we propose a method that provides information on the pump flows, the inflow to a sewage pit, and an online estimate of the efficiency of the pump. All these information are obtained without a flow sensor. We argued that the calculated flow values can be used by the sewer management to optimize the operation on the sewer pumps, and the efficiency estimate can be used for optimal scheduling of maintain procedures. The flow and the efficiency estimations are exemplified on a pumping station of the sewer network in Herning, Denmark.

scholarly journals Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation

2021 ◽  
Vol 11 (6) ◽  
pp. 2599
Author(s):  
Felix Nobis ◽  
Felix Fent ◽  
Johannes Betz ◽  
Markus Lienkamp
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
State Of The Art ◽  
Radar Data ◽  
Quality Data ◽  
Data Set ◽  
3D Object ◽  
The Public ◽  
3D Object Detection

State-of-the-art 3D object detection for autonomous driving is achieved by processing lidar sensor data with deep-learning methods. However, the detection quality of the state of the art is still far from enabling safe driving in all conditions. Additional sensor modalities need to be used to increase the confidence and robustness of the overall detection result. Researchers have recently explored radar data as an additional input source for universal 3D object detection. This paper proposes artificial neural network architectures to segment sparse radar point cloud data. Segmentation is an intermediate step towards radar object detection as a complementary concept to lidar object detection. Conceptually, we adapt Kernel Point Convolution (KPConv) layers for radar data. Additionally, we introduce a long short-term memory (LSTM) variant based on KPConv layers to make use of the information content in the time dimension of radar data. This is motivated by classical radar processing, where tracking of features over time is imperative to generate confident object proposals. We benchmark several variants of the network on the public nuScenes data set against a state-of-the-art pointnet-based approach. The performance of the networks is limited by the quality of the publicly available data. The radar data and radar-label quality is of great importance to the training and evaluation of machine learning models. Therefore, the advantages and disadvantages of the available data set, regarding its radar data, are discussed in detail. The need for a radar-focused data set for object detection is expressed. We assume that higher segmentation scores should be achievable with better-quality data for all models compared, and differences between the models should manifest more clearly. To facilitate research with additional radar data, the modular code for this research will be made available to the public.

scholarly journals Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders

2020 ◽  
Vol 34 (05) ◽  
pp. 9668-9675
Author(s):  
Yanbin Zhao ◽  
Lu Chen ◽  
Zhi Chen ◽  
Kai Yu
Keyword(s):  
State Of The Art ◽  
Automatic Generation ◽  
Complex Sentences ◽  
Text Simplification ◽  
Parallel Data ◽  
Different Types ◽  
Back Translation ◽  
Multiple State ◽  
Simple Pair

Text simplification (TS) rephrases long sentences into simplified variants while preserving inherent semantics. Traditional sequence-to-sequence models heavily rely on the quantity and quality of parallel sentences, which limits their applicability in different languages and domains. This work investigates how to leverage large amounts of unpaired corpora in TS task. We adopt the back-translation architecture in unsupervised machine translation (NMT), including denoising autoencoders for language modeling and automatic generation of parallel data by iterative back-translation. However, it is non-trivial to generate appropriate complex-simple pair if we directly treat the set of simple and complex corpora as two different languages, since the two types of sentences are quite similar and it is hard for the model to capture the characteristics in different types of sentences. To tackle this problem, we propose asymmetric denoising methods for sentences with separate complexity. When modeling simple and complex sentences with autoencoders, we introduce different types of noise into the training process. Such a method can significantly improve the simplification performance. Our model can be trained in both unsupervised and semi-supervised manner. Automatic and human evaluations show that our unsupervised model outperforms the previous systems, and with limited supervision, our model can perform competitively with multiple state-of-the-art simplification systems.

scholarly journals Analisis Kualitas Pelayanan Penerbitan Rekomendasi Izin Usaha Mikro Pada Kantor Camat Lae Parira Kabupaten Dairi

PERSPEKTIF ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 262-271
Author(s):  
Andy Penta Gracia Simbolon ◽  
Badaruddin Badaruddin ◽  
Nina Siti Salmaniah Siregar
Keyword(s):  
Quality Of Service ◽  
Research Method ◽  
Qualitative Method ◽  
Public Image ◽  
Service Work ◽  
Quality Of Services ◽  
District Office ◽  
The Public ◽  
Constraint Factors

The purpose of the study was to determine and analyze the quality of service for issuing micro business license recommendations and their constraints at the Lae Parira sub-district office, Dairi Regency. The research method used in this research is descriptive qualitative method, which is a method that only describes situations and events that aims to systematically describe the characteristics of a population or certain fields in a factual and accurate manner without looking for or explaining a relationship. The results of the study found that the quality of service for issuing recommendations for micro business licenses at the Lae Parira District Office of Dairi Regency was still not good. This can be seen from the complaints of the public or micro business actors who require licensing recommendations so that the general public has a negative view or picture of the agency. The constraint factors faced in improving the quality of services for issuing recommendations for micro business licenses are: the presence of officers who seek to obtain personal benefits from the licensing recommendation service process, employees do not try to avoid a negative public image of the institution so that many people are reluctant to deal with business administration, and lack of employee commitment to improving service quality so that employees tend to prioritize personal matters over service work to the community

scholarly journals The professionalization of educators in science museums and centers

2008 ◽  
Vol 07 (04) ◽  
pp. C02
Author(s):  
Lynn Uyen Tran
Keyword(s):  
Significant Role ◽  
State Of The Art ◽  
Research Literature ◽  
The State ◽  
Science Museums ◽  
Research Attention ◽  
The Public ◽  
Shared Identity ◽  
Rich Diversity

Explainers have a longstanding presence in science museums and centres, and play a significant role in the institutions’ educational agenda. They interact with the public, and help make visitors’ experiences meaningful and memorable. Despite their valuable contributions, little research attention has been paid to the role and practice of these individuals. From the limited research literature that does exist, we know that museum educators employ a complexity of skills and knowledge. We also know such educators have a variety of experiences and qualifications – this creates a rich diversity within the field. Finally we know that the content and quality of programmes designed to educate novice explainers vary across institutions. Should we work toward a shared identity across institutions? Or even a “professionalization”? The paper explores the state of the art of the discussion around that questions.

Synthesis and applications of nanoparticles: State of the Art and Future Perspective

2021 ◽  
Vol 11 ◽  
Author(s):  
Smriti Shukla ◽  
Mitali Sharma ◽  
Sapna Yadav ◽  
Avinash Raghupathy ◽  
Kartikeya Shukla ◽  
...  
Keyword(s):  
Water Quality ◽  
Human Health ◽  
State Of The Art ◽  
Crop Plants ◽  
The Other ◽  
Future Perspective ◽  
Different Types ◽  
Different Doses ◽  
Soil And Water

: Nanoparticles are being extensively studied these days to grab more knowledge on how they can be used in various fields to increase the yields and hence be beneficial for biotic components of the ecosystem. Chemicals being used in agriculture have caused a lot of damage to the soil and water quality along with the crop plants, ultimately affecting human health severely. Better alternatives are thus required to get higher yields with a better quality of crop plants that enhance human health. A variety of nanoparticles exists in nature, while others have been manufactured accidentally or engineered purposefully. These can play many beneficial roles in the crop plants, increasing the yield of crops and quality of the grains. They can be applied at various stages and in different doses. The effect they exhibit would be dependent on many factors. Different nanoparticles have diverse effects on different plants. Some nanoparticles may be beneficial to one species of crop plant and disadvantageous to the other one. Therefore, an elaborative study is required on all the types of nanoparticles exhibiting their advantageous and disadvantageous impacts on different species of crop plants for the dose and stage in which they have been applied. This review explains the different types of nanoparticles categorized based on their manufacture and the different effects they cause in different plant species. More research and knowledge is yet to be obtained before using nanoparticles in crop plants since the way they affect human health is a serious matter of concern.

scholarly journals Question-driven summarization of answers to consumer health questions

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Max Savery ◽  
Asma Ben Abacha ◽  
Soumya Gayen ◽  
Dina Demner-Fushman
Keyword(s):  
Deep Learning ◽  
Health Information ◽  
Gold Standard ◽  
State Of The Art ◽  
Learning Approaches ◽  
Consumer Health ◽  
Automatic Summarization ◽  
Medical Expertise ◽  
Different Types

Abstract Automatic summarization of natural language is a widely studied area in computer science, one that is broadly applicable to anyone who needs to understand large quantities of information. In the medical domain, automatic summarization has the potential to make health information more accessible to people without medical expertise. However, to evaluate the quality of summaries generated by summarization algorithms, researchers first require gold standard, human generated summaries. Unfortunately there is no available data for the purpose of assessing summaries that help consumers of health information answer their questions. To address this issue, we present the MEDIQA-Answer Summarization dataset, the first dataset designed for question-driven, consumer-focused summarization. It contains 156 health questions asked by consumers, answers to these questions, and manually generated summaries of these answers. The dataset’s unique structure allows it to be used for at least eight different types of summarization evaluations. We also benchmark the performance of baseline and state-of-the-art deep learning approaches on the dataset, demonstrating how it can be used to evaluate automatically generated summaries.

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